DOI QR코드

DOI QR Code

On Modification and Application of the Artificial Bee Colony Algorithm

  • Ye, Zhanxiang (Dept. of Information Technology, Wenzhou Vocational and Technical College) ;
  • Zhu, Min (Dept. of Information Technology, Wenzhou Vocational and Technical College, College of Internet of Things, Nanjing University of Posts and Telecommunications) ;
  • Wang, Jin (School of Computer & Communication Engineering, Changsha University of Science & Technology)
  • Received : 2018.01.08
  • Accepted : 2018.02.26
  • Published : 2018.04.30

Abstract

Artificial bee colony (ABC) algorithm has attracted significant interests recently for solving the multivariate optimization problem. However, it still faces insufficiency of slow convergence speed and poor local search ability. Therefore, in this paper, a modified ABC algorithm with bees' number reallocation and new search equation is proposed to tackle this drawback. In particular, to enhance solution accuracy, more bees in the population are assigned to execute local searches around food sources. Moreover, elite vectors are adopted to guide the bees, with which the algorithm could converge to the potential global optimal position rapidly. A series of classical benchmark functions for frequency-modulated sound waves are adopted to validate the performance of the modified ABC algorithm. Experimental results are provided to show the significant performance improvement of our proposed algorithm over the traditional version.

Keywords

References

  1. Y. Nie, Z. Zhang, H. Sun, T. Su, and G. Li, "Homography propagation and optimization for widebaseline street image interpolation," IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 10, pp. 2328-2341, 2017. https://doi.org/10.1109/TVCG.2016.2618878
  2. A. Sizov, K. A. Lee, and T. Kinnunen, "Direct optimization of the detection cost for I-vector-based spoken language recognition," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 3, pp. 588-597, 2017. https://doi.org/10.1109/TASLP.2017.2651377
  3. S. Jiang, M. Liu, and J. Hao, "A two-phase soft optimization method for the uncertain scheduling problem in the steelmaking industry," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 3, pp. 416-431, 2017. https://doi.org/10.1109/TSMC.2015.2503388
  4. W. Wu, B. Wang, Y. Zeng, H. Zhang, Z. Yang, and Z. Deng, "Robust secure beamforming for wireless powered full-duplex systems with self-energy recycling," IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 10055-10069, 2017. https://doi.org/10.1109/TVT.2017.2744982
  5. W. Wu and B. Wang, "Efficient transmission solutions for MIMO wiretap channels with SWIPT," IEEE Communications Letters, vol. 19, no. 9, pp. 1548-1551, 2015. https://doi.org/10.1109/LCOMM.2015.2451179
  6. M. S. Kiran, O. Findik, "A directed artificial bee colony algorithm," Applied Soft Computing, vol. 26, pp. 454-462, 2015. https://doi.org/10.1016/j.asoc.2014.10.020
  7. I. Babaoglu, "Artificial bee colony algorithm with distribution-based update rule," Applied Soft Computing, vol. 34, pp. 851-861, 2015. https://doi.org/10.1016/j.asoc.2015.05.041
  8. Y. Shi, C. M. Pun, H. Hu, and H. Gao, "An improved artificial bee colony and its application," Knowledge-Based Systems, vol. 107, pp. 14-31, 2016.